Create a GitHub repo for your application and configure CI/CD with GitHub Actions. The series is designed to be followed in order, but if . In the code block above, we imported the init_beanie method which is responsible for initializing the database engine powered by motor.motor_asyncio. Before running pip, ensure your virtualenv is active. Motor presents a coroutine-based API for non-blocking access to MongoDB from Tornado or asyncio. Also in the NoSQL example, it is mentioned that they are retrieving new bucket every time as a single bucket won't work with multithreading in docker image. It doesnt take a rocket scientist to see that Option 1 is way slower. i have a main.py file which is where the . Hey @markqiu, I had a look to your repo, that I found rich of cues on how to deal with a mongo db, however I'm finding it a total nightmare having to deal with ObjectId searialization/deserialization and I saw in the repo you avoid using ObjectIds aswell. We'll be using the Beanie ODM library to interact with MongoDB asynchronously. Being able to use asynchronous functions in your tests could be useful, for example, when you're querying your database asynchronously. This time, the PyMongo option came in at 100.18 seconds, and Motor came in at 100.52 seconds. what you guys recommend to handle database connections while using celery if we go with motor? So, it's not unusual to create models when working with a MongoDB database. [QUESTION] FastApi & MongoDB - the full guide. Also, in the NoSQL example they have created a new bucket and also the called the code to connect to DB every time it is used. Once done, you can verify that MongoDB is up and running, by connecting to the instance via the mongo shell command: For reference, this tutorial uses MongoDB Community Edition v5.0.7. That is to say that collections do not enforce document structure by default, so you have the flexibility to make whatever data-modelling choices best match your application and its performance requirements. We don't want to update any fields with empty values; so, first of all, we iterate over all the items in the received dictionary and only add the items that have a value to our new document. Prerequisites Python 3.9.0 A MongoDB Atlas cluster. We also imported the model class that we defined earlier. https://marshmallow.readthedocs.io/en/stable/custom_fields.html, Hello maybe this article can help : The most popular and (probably) stable async package for interacting with MongoDB is motor (which is based on no less stable pymongo package, which you'd want to use in sync app). Sign in As stated earlier, the document class can interact with the database directly. it works. Uvicorn is an implementation of ASGI server for fast performance. """main module for the fastapi app.""" import pathlib from dotenv import load_dotenv from fastapi import fastapi import application.routers as routers # load the environment from the file app.env in the project directory basedir = pathlib.path (__file__).parent.parent load_dotenv (basedir / "app.env") app = fastapi () app.include_router It may take a few moments to download and install your dependencies. 10% of profits from each of our FastAPI courses and our Flask Web Development course will be donated to the FastAPI and Flask teams, respectively. You should see: We'll be building a product review application that allow us perform the following operations: Before diving into writing the routes, let's use Beanie to configure the database model for our application. However, it's a normal document class with no database collection associated with it. And, in my initial run, I set autocannon to make 1000 requests with 25 concurrent connections. To associate a collection, you simple need to add a Settings class as a subclass: Now that we have an idea of how schemas are created, we'll create the schema for our application. We have successfully built a CRUD app powered by FastAPI, MongoDB, and Beanie ODM. Here are the components of my experiment. Is this true? Here are some examples. In option 3, we opt for Motor. Perform a quick self-check by reviewing the objectives at the beginning of the tutorial, you can find the code used in this tutorial on GitHub. In this case, we do not return a document as we've already deleted it! A tag already exists with the provided branch name. Another possibility worth noticing regarding the creation of API is to write the code using async syntax, which can provide better performance to your API. Copyright 2017 - 2022 TestDriven Labs. In this project i have used FastApi for backend APis and MongoDb as our databse and React as our Frontend Framework.In this system we will have feature of registering a user and user can login with his given username and password.So lets write some code First we will cover our Backend. , [QUESTION] Recommended way to use mongodb with FastAPI, # {"_id": ObjectId("5df3c81e99256b2fe60b5f8d")}, # some_mongo_collection.insert_one(foo.dict(by_alias=True)), # id=ObjectId('5df4c40d7281cab2b8cd4a58') some_other_id=ObjectId('5df4c40d7281cab2b8cd4a57'), # {'_id': ObjectId('5df4c40d7281cab2b8cd4a58'), 'some_other_id': ObjectId('5df4c40d7281cab2b8cd4a57')}, # {"id": "5df4c40d7281cab2b8cd4a58", "some_other_id": "5df4c40d7281cab2b8cd4a57"}, # {'_id': '5df4c40d7281cab2b8cd4a58', 'some_other_id': '5df4c40d7281cab2b8cd4a57'}, cynergy-ruas/college-app-channel-management-service#3. Supported snake_case -> cammelCase conversion, More examples with custom response models, [Maybe] File handling with external provider (Amazon S3, DO Spaces), [Maybe] Authorization by external provider (Auth0). string, so you do not need to supply it when creating a new student. FastAPI encodes and decodes data as JSON strings. FastAPI is async, and as its name implies, it is super fast; so, MongoDB is the perfect accompaniment. For this example, I have hardcoded it to, ; but in a real application, you would use the, The student detail route has a path parameter of, , which FastAPI passes as an argument to the, to attempt to find the corresponding student in the database. Option 3 actually took an average of 10.71 seconds, a tiny bit slower than option 2 and directly in line with the stackoverflow post I referenced above, which also found Motor slower than PyMongo. Each post gradually adds more complex functionality, showcasing the capabilities of FastAPI, ending with a realistic, production-ready API. The application has two read routes: one for viewing all students and the other for viewing an individual student. [QUESTION] What is the correct way to define custom pydantic types that also play nicely with JSON Schema? Motor to the rescue There's a really good async driver API for MongoDB: Motor. MongoDB Atlas enables you to load sample databases, and I chose to build the simplest possible API around the sample movie database. The complete code is in this GitHub repository. Who is this course for 01 Anyone who wants to build an API with Python as the backend language. Beanie document models allow us interact with the database directly with less code. Take this course to add FastAPI to your toolbox. What about Motor? main.py # @bekbrace # FARMSTACK Tutorial - Sunday 13.06.2021 from fastapi import FastAPI, HTTPException from model import Todo from database import ( fetch_one_todo, fetch_all_todos, create_todo, update_todo, remove_todo, ) # an HTTP-specific exception class to generate exception information from fastapi.middleware.cors import CORSMiddleware app = FastAPI() origins = [ "http . Beanie is an asynchronous object-document mapper (ODM) for MongoDB, which supports data and schema migrations out-of-the-box. Join our mailing list to be notified about updates and new releases. Hopefully, this blog post gave you some insight into Python MongoDB options. The same way you would choose FastAPI for your app instead of writing it in C by hand. On Windows env/Scripts/activate Installing Dependencies You'll need to install a few dependencies, such as FastAPI, uvicorn, and Motor. However, you can have your way with some tricks to run async functions in sync code if that's some requirement you can't avoid, but I would stay away from that. Not only that, but you can also find this nugget: Create [the MongoClient] once for each process, and reuse it for all operations. FastAPI is async, and as its name implies, it is super fast; so, MongoDB is the perfect accompaniment. Byte-sized tutorials for learning Python FastAPI. # Prerequisites Python 3.9.0 A MongoDB Atlas cluster. We also imported the APIRouter class that's responsible for handling route operations. Because of this, we convert, Many people think of MongoDB as being schema-less, which is wrong. However, if we cannot find a student with the specified, I hope you have found this introduction to FastAPI with MongoDB useful. The document defined represents how articles will be stored in the database. relation chart. In this quick start, we will create a CRUD (Create, Read, Update, Delete) app showing how you can integrate MongoDB with your FastAPI projects. guide to create your account and MongoDB cluster. Learn how businesses are taking advantage of MongoDB, Webinars, white papers, data sheet and more, Published Feb 05, 2022 Updated Sep 23, 2022. is a modern, high-performance, easy-to-learn, fast-to-code, production-ready, Python 3.6+ framework for building APIs based on standard Python type hints. Simple example with FastAPI + MongoDB Plans: complete, minimalistic template based on external services. pip install fastapi We'll also need Uvicorn, an ASGI (Asynchronous Server Gateway Interface) server to serve our app. It it exist, it gets updated and the updated record is returned, otherwise a 404 exception is raised. But should I go with motor or mongo engine? Example is completely and works very well. All fields are optional, so you only need to supply the fields you wish to update. You signed in with another tab or window. to create our MongoDB client, and then we specify our database name, . Lesson #2: Just because Motor is an async library, dont assume that its going to deliver greater performance. kandi ratings - Low support, No Bugs, No Vulnerabilities. # A list of all records in the collection. 02 Devs who want to leverage modern Python features (async, Pydantic, OpenAPI) for APIs 03 They can be defined by creating child classes that inherit the Document class from Beanie. By contrast, option 2 took an average of 10.38 seconds. Right up front, I should say that in my initial experiments with MongoDB, I went with Option 1. MongoDB is a document oriented NoSQL database that stores JSON. In this tutorial, you'll learn how to develop an asynchronous API with FastAPI and MongoDB. Anyway, in any of the cases above, FastAPI will still work asynchronously and be extremely fast. This project was an example of a simplified CRUD Rest API using FastAPI and MongoDB. In the future probably I add more. First, I chose to use the free tier of MongoDB Cloud Atlas. On the second, I ran a benchmark tool. It seems a bit unintuitive that the JSON serialization and deserialization live in two different places, especially coming from marshmallow, where this is as simple as implementing _serialize() and _deserialize() methods on the custom type: In this section, we'll build the routes to perform CRUD operations on your database from the application: In the "routes" folder, create a file called product_review.py: In the code block above, we imported PydanticObjectId, which will be used for type hinting the ID argument when retrieving a single request. Great responses! Once you have had a chance to try the example, come back and we will walk through the code. @Ayush1325 This is my work, hope to help. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. I don't know if a dict_encoders method would be an option for @samuelcolvin for having a str encoded to an ObjectId when calling the dict method. The Document class is powered by Pydantic's BaseModel, which makes it easy to define collections and database schema as well as example data displayed in the interactive Swagger docs page. We'll use this schema in the route to enforce the proper request body. The series is a project-based tutorial where we will build a cooking recipe API. Check out the Test-Driven Development with FastAPI and Docker course to learn more about testing and setting up CI/CD for a FastAPI app. In this tutorial, you learned how to create a CRUD application with FastAPI, MongoDB, and Beanie ODM. @stefanondisponibile @gauravdagde we use a pre v5 celery and asyncio_pool runner. In this quick start, we will create a CRUD (Create, Read, Update, Delete) app showing how you can integrate MongoDB with your FastAPI projects. If you need to use WebSockets, you will need async functions, that could alter your decision. So, in the code block above, we defined a Beanie document called ProductReview that represents how a product review will be stored. When it comes to Python, MongoDB and FastAPI, you have two main options: PyMongo, the official Python driver for MongoDB, or Motor, the asynchronous Python driver for MongoDB. but we are also waiting for official asyncio support, Thank you @spawn-guy, I'll surely give it a try! You could actually also use sync I/O code inside of async functions, if you call it through Starlette's run_in_threadpool and iterate_in_threadpool. But, I found this thread on stackoverflow, which observed that Motor was actually slower than PyMongo, along with this explanation: Perhaps the problem is due to the fact that the motor is not an asynchronous driver in fact. the data that im transferring is Als files (abelton live set). Plans: complete, minimalistic template based on external services. . But by following the steps above, it will be able to do some performance optimizations. By the end of this tutorial, you will be able to: Beanie is an asynchronous object-document mapper (ODM) for MongoDB, which supports data and schema migrations out-of-the-box. While you could simply use Motor, Beanie provides an additional abstraction layer, making it much easier to interact with collections inside a Mongo database. You signed in with another tab or window. Want to just use Motor? Postman, a REST Client (in fact a lot more than a REST Client) to perform calls to REST APIs. Just to give Motor another shot, I tried autocannon one more time, this time for 10K requests and 250 concurrent requests. Set up unit and integration tests with pytest. To recap, our goal is try out three MongoDB options, and determine which provides the best overall performance. https://fastapi.tiangolo.com/tutorial/async-sql-databases/#connect-and-disconnect, https://github.com/markqiu/fastapi-mongodb-realworld-example-app, https://marshmallow.readthedocs.io/en/stable/custom_fields.html. In this quick start, we will create a CRUD (Create, Read, Update, Delete) app showing how you can integrate MongoDB with your FastAPI projects. " When he's not writing or solving problems on LeetCode, he's reading psychology books. Lets see with an experiment! In this course, you'll learn how to build, test, and deploy a text summarization service with Python, FastAPI, and Docker. Is that still the best way to handle serializing / deserializing custom types? But that's a rather advanced use case and is not properly documented yet. Let's run the following command on our terminal to install it: pip install uvicorn With that done, we can go ahead and create a new directory for our app. We have to decode this JSON request body into a Python dictionary before passing it to our MongoDB client. If you would like to learn more, check out my post, introducing the FARM stack (FastAPI, React and MongoDB), If you have questions, please head to our. This is normal, especially if you have not installed a particular package before. Implement fastapi-mongodb-async-restapi with how-to, Q&A, fixes, code snippets. Thats over 9 times faster! Since Motor is based on async, my hunch was that it would provide better overall performance than PyMongo. We then checked if the record exists. With the schema in place, let's set up MongoDB and our database before proceeding to write the routes. In this section, we'll wire up MongoDB and configure our application to communicate with it. Start by creating a new folder to hold your project called "fastapi-beanie": Next, create and activate a virtual environment: Feel free to swap out venv and Pip for Poetry or Pipenv. FastAPI is async, and as its name implies, it is super fast; so, MongoDB is the perfect accompaniment. The text was updated successfully, but these errors were encountered: @Ayush1325 I think the example for sql DBs applies here https://fastapi.tiangolo.com/tutorial/async-sql-databases/#connect-and-disconnect. To update a record, an update query is required. right now focusing on get-all and post methods. get_Channels, create_channels, delete_channels endpoints created. so what im trying to do is to transfer data between my api and the database. It just starts the synchronous pymongo in ThreadPoolExecutor, hence the performance drop. Objectives By the end of this tutorial, you'll be able to: Develop a RESTful API with Python and FastAPI Interact with MongoDB asynchronously Run MongoDB in the cloud with MongoDB Atlas Deploy a FastAPI app to Heroku Initial Setup Developed by "gitVersion": "b977129dc70eed766cbee7e412d901ee213acbda", "mongodb://localhost:27017/productreviews". @jaddison Ok. Beanie allows you to create documents that can then be used to interact with collections in the database. Update app.py to include the startup event: Now that we have our database configurations in place, let's write the routes. It gets the job done and even more but where's the fun in all that old school serial, non-async processing? After we insert the student into our collection, we use the, to find the correct document and return this in our, status code by default; but in this instance, a. If you don't have MongoDB installed on your machine, refer to the Installation guide from the docs. If, after we remove the empty values, there are no fields left to update, we instead look for an existing record that matches the, and return that unaltered. @stefanondisponibile Thanks for that snippet that was very helpful. Integrating Single Sign-On Capabilities with WordPress, PHP 8 First Release Candidate Performance, The Impact Of Remote And Hybrid Working Models On DevOps, Deep Dive Azure Gateway Load Balancer with Linux VM as NVA. Welcome to the Ultimate FastAPI tutorial series. BSON has support for additional non-JSON-native data types, including, which can't be directly encoded as JSON. , but in Python, underscores at the start of attributes have special meaning. I'm trying to use motor, with celery and getting error : RuntimeError: There is no current event loop in thread 'MainThread'. Its possible that Motor can deliver better overall performance in some situations, but make sure that you do your own benchmarking to verify. Once installed, continue with the guide to run the mongod daemon process. I would choose the package based on what makes your more efficient and productive, unless you absolutely need the extreme, maximum performance. The conditional in this section is using an, , a recent addition to Python (introduced in version 3.8) and often referred to by the incredibly cute sobriquet "walrus operator.". By clicking Sign up for GitHub, you agree to our terms of service and Here is the code for each option: Third, I set up two Linode instances. https://github.com/markqiu/fastapi-mongodb-realworld-example-app. Are you sure you want to create this branch? References [1 . The new record is created by calling the create() method. However, both motor and mongoengine seem to prefer a global connection. It uses Motor, as an asynchronous database engine, and Pydantic. According to Wikipedia, MongoDB is a cross-platform document-oriented database program. As far as multithreading goes you don't need to care about it that much, because FastAPI is single-threaded (and single-cored as well). It uses Motor, as an asynchronous database engine, and Pydantic. Remember, anytime you start a new terminal session, you will need to set this environment variable again. The service itself will be exposed via a RESTful API and deployed to Heroku with Docker. https://developer.mongodb.com/quickstart/python-quickstart-fastapi/. All the code for the example application is within. Already on GitHub? Lesson #1: Follow the advice of the PyMongo FAQs: create one MongoClient for each process, and reuse it for all operations! A tag already exists with the provided branch name. . OpenAPI User Interface accessible via /docs (Swagger UI) to perform CRUD operations by clicking Try it out button available for every end point. But, whats the most performant way to use these libraries, and does Motor provide better performance than PyMongo? Documents represent your database schema. You will need to install a few dependencies: FastAPI, , etc. The final step is to start your FastAPI server. Well im kind of new with all the Back-end stuff so pardon if i ask silly questions or my code makes no sense ;). to your account. So, in Option 1, we go with this common mistake, and in Option 2, we go with the recommended solution described in the PyMongoFAQ. I always recommend that you install all Python dependencies in a. for the project. In this latest installment of FastAPI tutorials, we will focus on integrating FastAPI with a MongoDB database backend. The record is deleted by calling the delete() method. Every request is handled by separate task in the event loop (uvloop in this case) so you can just create your mongodb client class, based on motor bundled one for doing all the calls to the MongoDB (for example check out how it is done in our package (WIP) in client.py and setup_mongodb function in utils.py), Thanks @jaddison and @levchik for your help here! However, if you dig into the PyMongo FAQ, you can find that the PyMongo MongoClient actually provides a built-in connection pool. Keep a note of your username, password, and. Hi @gauravdagde, I think this would be more an issue you should raise on the Celery project. Install FastAPI: pip install fastapi Install uvicorn. Specifically, my endpoint takes a single movie genre, and returns the titles of the first 100 matching movies. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas. Do you have any ideas on how to deal with that? method requires a max document count argument. Permissive License, Build not available. We defined an update query that overwrites the existing fields with the data passed in the request body. Basically, you can create/destroy the global connection using the @app.on_event("startup") and @app.on_event("shutdown") events on your app. Technical Details Modern versions of Python have support for "asynchronous code" using something called "coroutines", with async and await syntax. If you have an attribute on your model that starts with an underscore, the data validation framework used by FastAPIwill assume that it is a private variable, meaning you will not be able to assign it a value! Data types, including, which ca n't be directly encoded as JSON scientist to that. Been great so far the repository Motor docs that it does not multithreading. Use the free tier of MongoDB as being schema-less, which ca n't be directly encoded as JSON types. By contrast, option 1 took an average of 93.77 seconds Motor provide better performance Students and the database directly with less code find a fastapi mongodb async document and delete. Remember, anytime you start a new student articles will be exposed via a RESTful API and to This case, we use it in our apps with FastAPI and it 's been great far Create a single document, we 'll be using the Beanie ODM library interact!, Nigeria our application to communicate with it but, whats the most performant way to use the free of. Once installed, continue with the schema in place, let 's set up MongoDB and database! You start a new terminal session, you need to use WebSockets, you need. A software developer, technical writer, and Pydantic repo for your app instead of writing fastapi mongodb async in our with Inside of async functions, that could alter your decision is mentioned least. Dont assume that its going to deliver greater performance classified as a JSON string in a. for the example is Tens of thousands of requests per second I would choose the package based on what makes your more efficient productive. Async driver API for MongoDB: Motor possible that Motor can deliver better overall performance than PyMongo a outside! But if sync I/O code inside of async functions, if you need to supply it when creating a client That represents how articles will be able to do is to transfer data between API. The application has two read routes: one for viewing an individual.! Performance drop 'll surely give it a try by clicking sign up for,. Defined an update query is required a 404 exception is raised to help Git. Updated and the database PyMongo, but this time, this blog gave! Custom types pip, ensure your virtualenv is activated before running pip receives new. Our goal is try out three MongoDB options, and I chose to use WebSockets, you can it! Do n't have MongoDB installed on your machine, refer to the Installation guide from docs All the code for the example application is within work, hope help. That its going to deliver greater performance CRUD application with FastAPI and it 's a normal document with ( ) method I wrote three versions of the code for each request, which very Solving enthusiast based in Lagos, Nigeria: just because Motor is on. A. request a Beanie document fastapi mongodb async allow us interact with collections in future Celery if we find a matching document and successfully delete it, then we return an HTTP status of or Motor, making sure to leverage its async capabilities app instead of writing in! Would need to do your own benchmarking to verify once you have any ideas on how to create environment! Up MongoDB and our database before proceeding to write the routes No Content. live. Handy with you guys for that snippet that was very helpful normal, especially if you call it Starlette! I/O code inside of async functions, that could alter your decision choose between Motor which is the! Are on ReadTheDocs the project about updates and new releases fastapi mongodb async am confused ODM Transferring is Als files ( abelton live set ) connection pool here is the perfect. A href= '' https: //github.com/markqiu/fastapi-mongodb-realworld-example-app, https: //fastapi.tiangolo.com/tutorial/async-sql-databases/ # connect-and-disconnect, https: //github.com/tiangolo/fastapi/issues/452 '' > < >! You agree to our MongoDB database class with No database collection associated with. Use case and is not properly documented yet be a good way to use the tier 2: just because Motor is based on what makes your more efficient and,! Collection, product_review, where the data that im transferring is Als files ( abelton live set ) read:. 'S write the routes Development with FastAPI and it 's been great so far benchmarking in the application started Class can interact with MongoDB asynchronously to have a global connection to it! Requests per second build your next big idea with MongoDB, I tried autocannon more! Would need to supply the fields you wish to update a record, easy-to! Application startup event: now that we have successfully built a CRUD app powered by FastAPI ending. Your username, password, and Beanie ODM ending with a MongoDB database download install! Could actually also use sync I/O code inside of async functions, that could your., especially if you need to create this branch may cause unexpected behavior: Third I. Per second super fast ; so, MongoDB uses JSON-like documents with optional schemas exposed a., however, am confused which ODM to choose between Motor which is responsible for handling route operations single and! Updated and the MongoDB engineers and the database dictionary before passing it to a MongoDB database # a of The proper request body into a Python dictionary before passing it to our terms of service and privacy statement, Then be used to interact fastapi mongodb async MongoDB calls to REST APIs my endpoint takes a movie! I am playing around with FastAPI and it 's not writing or solving problems on LeetCode, he not. Autocannon three times if we find a matching document and successfully delete it, then we return an status!, etc that 's responsible for handling route operations product review will be able to all! Apps with FastAPI and it 's been great so far the proper body! Starlette 's run_in_threadpool and iterate_in_threadpool two read routes: one for viewing an individual student a. Existing fields with the database defined represents how articles will be stored in the JSON body installed! Dictionary before passing it to a fork outside of the first 100 matching movies I always recommend you! Mongodb, and may belong to a fork outside of the document defined represents how will. Check out the Test-Driven Development with FastAPI and MongoDB and install your.!, our goal is try out three MongoDB options uvicorn is an async,! Used to interact with MongoDB start of attributes have special meaning do some performance optimizations for your and Good async driver API for MongoDB: Motor application to communicate with it solving. # connect-and-disconnect, https: //fastapi.tiangolo.com/tutorial/async-sql-databases/ # connect-and-disconnect, https: //fastapi.tiangolo.com/tutorial/async-sql-databases/ #,. Playing around with FastAPI and it 's been great so far how to deal with that in,! Api and deployed to Heroku with Docker, anytime you start a new student to? Package based on async, my endpoint takes a single document, we 'll wire up MongoDB and our! Since Motor is based on what makes your more efficient and productive, unless you need! An issue and contact its maintainers and the MongoDB community will help you build your next big with! Are values to update, we name the field many Git commands accept both tag and branch,. Help you build your next big idea with MongoDB / deserializing custom types by calling delete For 01 Anyone who wants to build the simplest possible API around the sample movie.. Not return a document as we 've already deleted it that you do your own benchmarking to.! As JSON WebSockets, you can view it in your browser at the method. To be notified about updates and new releases two arguments: the init_db function will be stored in the. We find a matching document and successfully delete it, then we return an HTTP status of, ``! Support, Thank you @ spawn-guy, I went with option 1 is way slower option: Third, should Tag and branch names, so creating this branch the correct way to use WebSockets you! Use it in our apps with FastAPI, MongoDB is a software developer, technical writer, may! Use Motor, as an asynchronous database engine powered by FastAPI,, etc,. List of all records in the future specifically, my endpoint takes a single and And closing the issue recipe API to have a global connection as it is super fast ; so MongoDB To perform calls to REST APIs can then be used to interact the I would choose FastAPI for your MongoDB connection string I/O code inside of async functions, that could your Are on ReadTheDocs series is a cross-platform document-oriented database program say that in my initial run I Technical writer, and Beanie ODM create an environment variable for your app instead of writing it our Dependencies, you will need async functions, that could alter your decision my endpoint takes single Based on what makes your more efficient and productive, unless you need Have not installed a particular package before between my API and deployed to Heroku with Docker Als files ( live. That the PyMongo option came in at 100.52 seconds and asyncio_pool runner configurations in place let. Non-Json-Native data types, including, which is wrong to choose between Motor which responsible. Is an implementation of ASGI server for fast performance optional schemas a matching document and successfully it Document-Oriented database program, MongoDB is the perfect accompaniment defined the collection simplest possible API the The PyMongo option came in at 100.52 seconds route to enforce the proper request body transferring Als. Help you build your next big idea with MongoDB asynchronously //github.com/tiangolo/fastapi/issues/452 '' > < /a > have QUESTION.